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Predictive Analytics 1 with R – Machine Learning Tools

Predictive Analytics 1 with R – Machine Learning Tools

This course introduces to the basic predictive modeling paradigm: classification and prediction.


In this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. You will cover two core paradigms that account for most business applications of predictive modeling: classification and prediction. You will also study commonly used machine learning techniques and learn how to combine models to obtain optimal results. This course includes hands-on work with R, a free software environment with statistical computing capabilities.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

At the conclusion of this course you will be able to visualize and explore data, provide an assessment basis for predictive models, and choose appropriate performance measures. You will become familiar with common algorithms including k-nearest-neighbor, Naive Bayes, Classification and Regression Trees, as well as ensemble models.

  • Visualize and explore data to better understand relationships among variables
  • Organize the predictive modeling task and data flow
  • Develop machine learning models with the KNN, Naive Bayes and CART algorithms using R
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Use various R packages to implement the models in the course

Who Should Take This Course

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.  This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Our Instructors

Dr. Inbal Yahav

Dr. Inbal Yahav

Dr. Inbal Yahav is a faculty member at the Graduate School of Business Administration, Bar-Ilan University, Israel.  Her research interests lie in the areas of statistical modeling and social media, with a focus on users’ behavior in social networks, interactions and dynamics among users, and statistical modeling of heterogeneous behaviors.  Dr. Yahav’s research to-date focuses on two domains. The first domain is cyber security and privacy, and in specific privacy unawareness and unintentional information leakage in social networks. The second domain is statistical modeling of sub-populations in big data. Dr. Yahav has presented her work at multiple Information Systems conferences and has published papers in books and journals.    

Course Syllabus

Week 1


  • What is supervised learning
  • Data partitioning and holdout samples
  • Choosing variables (features)
  • Handling missing data
  • Visualization and exploration

Week 2

Classification and Prediction

  • Assessing classification models
    • Confusion matrix
    • Misclassification costs
    • Lift
  • Assessing prediction models
    • Common metrics
  • K-Nearest-Neighbors (KNN)
    • Measuring distance
    • Choosing k
    • Generating classifications and predictions

Week 3

Bayesian Classifiers; CART

  • Full Bayes classifier
  • Naive Bayes classifier
  • Classification and Regression Trees (CART)
    • Growing the tree
    • Avoiding overfit – pruning
    • Using trees for classifications and predictions

Week 4


  • Combine multiple algorithms
  • Improve results

Class Dates


01/12/2024 to 02/09/2024
Instructors: Dr. Inbal Yahav
05/10/2024 to 06/07/2024
Instructors: Dr. Inbal Yahav
09/13/2024 to 10/11/2024
Instructors: Dr. Inbal Yahav


01/10/2025 to 02/07/2025
Instructors: Dr. Inbal Yahav
05/09/2025 to 06/06/2025
Instructors: Dr. Inbal Yahav
09/12/2025 to 10/10/2025
Instructors: Dr. Inbal Yahav


You should be familiar with R.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Predictive Analytics 1 with R – Machine Learning Tools

Additional Information

Time Requirements



Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project. Note: There will be a mid-week discussion exercise in the first week of the course.

In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.

Course Text

The recommended text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R,  by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R”


This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)



Take a 10-question quiz on analytics: Test Yourself

Whatch our preview of this course:


Watch this video by Dr. Shmueli on “Data Mining in a Nutshell”.

Register For This Course

Predictive Analytics 1 with R – Machine Learning Tools